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				<div class="title-inner">Named Entity Recognition</div>
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				Compare with <a target="_blank" class="js-corenlp" href="http://corenlp.run/#text=">CoreNLP’s results</a> on the same query. 
				Or compare with spaCy’s <a target="_blank" class="js-displacy" href="https://explosion.ai/demos/displacy-ent?text=">displaCy results</a> on entity recognition.
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				This is a demo of HMTL for NLP, our new NLP multi-task model that reaches or beats the state-of-the-art on 4 distinct NLP tasks. 
				The model combines Named Entity Recognition, Entity Mention Detection, Relation Extraction and Coreference Resolution. 
				These 4 tasks are all fundamentals for NLP and NLU applications such as conversational agents. 
				Moreover, these 4 tasks can benefit each other when trained together in a multi-task learning framework, a recent trend in NLP which has been driving a lot of top-notch research.
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				Our implementation is based on the <a target="_blank" href="https://allennlp.org">AllenNLP library</a> 
				from the <a target="_blank" href="https://allenai.org">Allen Institute for Artificial Intelligence</a>, and makes use of the ELMo embeddings. 
				<!--If you are interested in learning more about the model, we explain in a Medium blogpost the core components of the model and opensource the training code.-->
				If you like this demo, please share it and <a target="_blank" href="https://twitter.com/intent/tweet?url=https%3A%2F%2Fhuggingface.co%2Fhmtl&via=SanhEstPasMoi%20%40Thom_Wolf%20%40seb_ruder%20%40julien_c%20%40huggingface">tweet about it</a> 💛!
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